GOOGLE CLOUD MACHINE LEARNING ENGINE
Artificial Intelligence (AI) and Machine Learning (ML) are two technologies that are correlated with each other and are used for creating intelligent systems. Even though people use them as a synonym for each other, they are still two different terms.
AI is an aspect of computer science that enables computer systems to mimic or simulate human intelligence. It is referred to as ‘Artificial’ and ‘Intelligence’ because it is a ‘human-made intelligence.’ In comparison, ML is a part of AI, which makes it possible for a machine to learn automatically from past data without explicit programming.
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AI requires numerous computing resources, which cloud computing addresses, and adopting the power of AI is crucial for businesses in other to remain competitive through the use of and acquisition of intelligence from large amounts of data. AI has affected so many domains and industries, such as health care, finance, transportation, global warming, energy, astronomy, social network dynamics, and economic behavior. Google, as well as other companies, such as IBM, Amazon, and Microsoft, provide open-source AI platforms.
ML allows humans to push the boundaries of technology to conquer new realms of human knowledge and activities. This has been made possible with the many more powerful ML platforms and open tools that have been made available. ML allows users to better understand and predict human behavior from the comfort of their laptops at the home, office, or anywhere in the world.
ML and the Cloud
There are physical limitations in terms of storage when it comes to ML projects which are resource intensive. From storage to computational power, training models require resources which is impossible to be found on a standalone computer. However, these storage limitations have been reduced over the years as there is now reliable terabyte storage that can be accessible at lower prices, and computational power has increased because what could only work on expensive workstations a few years ago can now work on laptops. Even with the rapid revolution, the power of a standalone laptop is still limited because there is a limit to the amount of data that you can store on it. The same with AI, such as self-driving cars, chatbots, and speech-to-text, which also require even larger resources.
These limitations are not applicable to ML in the cloud. This is because computing in the cloud gives one direct access to high-performance computing (HPC). Cloud computing is built on a distributed architecture, and its processors are spread across different servers instead of a single machine. It does not need any software installation or storage on a computer hard drive before you can access it; all you need to do is to sign up to enjoy the free services online.
Why use Cloud Computing for ML?
The connection between cloud computing and ML is that ML requires enormous data storage, processing power, and numerous servers simultaneously in other to work on an algorithm. The cloud provides new servers consisting of pre-defined data and dynamic resources over the cloud (internet).
Cloud computing is used for the purpose of computation, and ML requires large computational power to develop sample data, and only a few people have access to many strong machines which have large computational power.
Pros of ML with Cloud Computing
Cloud computing provides the flexibility to use ML functionalities without having any advanced data science skills.
The cloud works on the principle of ‘pay for what you need,’ which means that businesses can make use of the opportunities offered by ML capabilities without much expenditure.
Top Types of Cloud Computing Platforms for ML
Amazon Web Services (AWS)
This cloud computing platform was developed by Amazon for ML in 2006. They offer products such as Amazon SageMaker for creating and training ML models, Amazon Forecast for forecast accuracy for ML models, Amazon Personalize for creating personal recommendations in the ML system, AWS Deep Learning AMIs to solve deep learning problems in ML, Amazon Polly for the conversion of text to speech format, and Amazon Augmented AI for the implementation of human reviews in ML models.
Microsoft Azure
Microsoft Azure is a cloud computing platform provided by Microsoft since 2010. It is very popular among data scientists and ML professionals for data analytics requirements. For example, the Microsoft Azure Cognitive Service offers intelligent cognitive services for ML apps, Microsoft Azure Bot Services develops smart and intelligent bot services for ML apps, Microsoft Azure Databricks offers Apache Spark-based analytics, Microsoft Azure Cognitive Search for mobile and web apps in ML, and Microsoft Azure ML for deploying ML models over the cloud.
IBM Cloud
This cloud was formally known as Bluemix, and it is IBM’s most famous open-source cloud computing platform, which includes several cloud delivery models that are public, private, and hybrid models.
Google Cloud (GCP)
GCP is a cloud computing platform owned by Tech Giant Google, developed in 2008. It offers its infrastructure to customers to develop ML models over the cloud.
Google Cloud Machine Learning Engine
The Google Cloud Machine Learning Engine or Cloud MLE is a Google Infrastructure. This means that it is a fully-managed service on Google Cloud Platform (GCP) for running machine learning training jobs and deploying ‘large-scale’ machine learning models. Cloud MLE is designed to facilitate the building and running of models on Google’s infrastructure without developers having comprehensive expertise in distributed computing or cloud infrastructure. Cloud MLE has provided the platform for businesses to build and train machine learning models through the integration of data analytics and storage cloud services such as Google BigQuery and Cloud Dataflow. Google also has dedicated machine learning educational and certification programs that businesses can use to learn.
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A fascinating feature of this platform is that it is scalable. Cloud MLE can handle complex models and large datasets, which makes it perfect for performing tasks such as natural language processing, image and speech recognition, and predictive analytics. It is also customizable because of its support for available ML frameworks and libraries such as Keras, TensorFlow, and scikit-learn. This makes it possible for developers to use frameworks that they are already familiar with to build and train models. Also, the Cloud MLE offers built-in support for distributed training which makes it possible for users to train models on large datasets over multiple machines. This can bring about a significant speed-up in the training process, which allows the possibility of training models on large datasets which would otherwise be too large to be done on a single machine. Cloud MLE also offers support for hyperparameter tuning. Hyperparameter tuning is the process of automatically finding the most reliable set of hyperparameters for a given model to optimize its performance. It provides built-in support for this process which makes it possible and easy to fine-tune the performance of a model without the need for manual experimentation.
After a model has been trained, Cloud MLE provides several tools that can be used to deploy it into a production environment. For example, online prediction allows users to send data to a deployed model and receive a real-time prediction which is useful for models which need to make predictions on a daily basis. It is used for applications like natural language processing and image recognition. In addition to online prediction, Cloud MLE also supports batch prediction, which enables users to send a large batch of data to the model and receive predictions for all of it at once. It can be used for customer segmentation and fraud detection, where the model needs to make predictions on a large dataset.
There are numerous useful resources available for building and deploying ML models. For example, cloud MLE consists of a Jupyter notebook environment which enables developers to easily write and execute codes for data exploration and model development. Also, there are a number of pre-trained models that provides a starting point for building new models and reducing development time. It consists of an AI Platform, which is a helpful tool that enables users to easily and quickly deploy, monitor, and maintain the models, which provides insights into the performance, accuracy, and other significant metrics. It also consists of another helpful tool called AutoML, which enables users to build models using a drag-and-drop interface instead of writing codes. This is helpful for anyone without any expertise in programming.
What are the Steps involved in the Training and Prediction from an ML model Deployed in Google Cloud ML Engine?
Data Preparation
This involves the acquisition of data and getting the data ready for ML experiments. This stage takes place outside the ML engine. GCP consists of numerous services that assist with data preparation. This stage involves the exploration and analysis of data quality which involves the transformation of the original dataset into a format that makes it easy to evolve a model. The typical steps involved in this process include missing data identification, splitting existing columns, duplicates removal, and so on. GCP services such as Cloud DataProc, BigQuery, Cloud Dataflow, and Cloud DataPrep are used to acquire and prepare the data. The final step involved in this stage is copying the prepared dataset to the Google Cloud Storage bucket, which makes it available to the distributed training job that is initiated by ML Engine. Also, datasets prepared outside of the Google Cloud can be uploaded to the Cloud Storage.
Model Creation
This is the stage where data scientists and developers code the model in their local environment. Cloud MLE supports Python-based toolkits used to create ML models. The supported frameworks and toolkits are XGBoost, Scikit-learn, and TensorFlow. A subset of the original dataset can be used by developers to test and debug codes before they are submitted as training jobs run in the cloud. Additionally, the Python programs written for the model creation might not have Cloud MLE-specific code. They may follow the standard conventions and flow used for creating ML models. When the code is created and tested, it becomes ready to be submitted to the ML Engine.
Model Training
This process involves feeding the training data into the model, evaluating it, and tuning the parameters to enhance accuracy. Here, the user feeds the known data points called features along with the original outcome as a label. In the evaluation stage, the user feeds the data from the test dataset and compares the predicted value with the actual label. This process is repeated until the variations between the actual labels and the predicted labels are minimum. ML Engine provides hyperparameter tuning for sophisticated models like artificial neural networks. The training process is stopped, and the final model becomes ready for consumption when the prediction matches the actual labels for most of the data points in the dataset. Also, based on the model complexity and dataset size, the training job can run across a cluster of machines that are backed by GPUs and TPUs. With ML Engine, you can select the right tier for scheduling the training job. ML Engine dynamically manages the compute resources needed for running the job.
Model Deployment
This stage involves the serialization of a model into a supported format which is uploaded to a Google Cloud Storage bucket. A model is registered in the Cloud MLE, pointing to the location of the Cloud Storage bucket where the serialized object is uploaded.
Predictions
Cloud MLE hosts fully trained models for predictions. There are two ways to get predictions from trained models; online and batch prediction. Online prediction, which is also called HTTP prediction, deals with one data point at a time, while batch prediction can deal with an entire dataset.
Monitoring
Cloud MLE predictions are tightly integrated with Stackdriver, which is the monitoring tool of GCP. With this, the user can monitor the predictions on an ongoing basis through APIs to examine running jobs.
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Conclusion
Using cloud computing for ML is very important because it provides an ideal environment for ML models having a large amount of data. Furthermore, the cloud can be used to train new systems, identify patterns, and make predictions. Also, Google Cloud MLE offers a scalable environment that can be used to collect, store, curate, and process data.
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